1. Field of Invention
The present invention relates generally to the field of audio pattern matching. More specifically, the present invention is related to matching heart sounds to previously diagnosed heart sounds assisting in physician decision.
2. Discussion of Related Art
Multimedia data is widely used in medical diagnosis through auditory and visual examination by physicians. In particular, heart auscultations and ECGs are two very important and commonly used diagnostic aids in cardiovascular disease diagnosis. While physicians routinely perform diagnosis by simple heart auscultation and visual examination of ECG waveform shapes, these two modalities reveal different diagnostic information about the heart. The heartbeat, for example, can reveal abnormal sounds caused by valvular disease, pericarditis, and dysrhythmia. On the other hand, the ECG unveils abnormal electrical activity during the contraction of the myocardium.
Single ECG analysis and ECG classification are well researched fields, and the popular techniques include neural network, machine learning methods, wavelet transforms and genetic algorithms. The rule-based methods rely on the accuracy of the P-Q-R-S-T segment detection. Errors in estimation of these feature values can cause major errors in disease-specific interpretation. The parametric modeling methods, on the other hand, are good at spotting major disease differences but can't take into account fine morphological variability due to heart rate (eg., ventricular vs. supra-ventricular tachycardia) and physiological differences. Related work in the time alignment of ECGs also exists.
Automatic analysis of heart sounds has been investigated for detecting heart abnormalities. The predominant approach in heart sound analysis is based on feature extraction and classification, as is conventional for audio analysis. These features can be roughly classified into two categories, the spatio-temporal features, such as the zero-crossing rate (ZCR), hidden Markov features etc., or frequency-domain features, such as Mel-frequency Cepstral Coefficients (MFCC).
U.S. Pat. Nos. 5,273,049; 7,031,765; and 6,480,737 describe matching electrocardiogram data to detect specific heart defects. These references do not match any sort of sound data. U.S. Pat. Nos. 5,218,969 and 5,025,809 process heart sound data to extract features (pitch/frequency, phase/sub-phase, etc.) from the heart sound and compare those extracted features to ranges of values which trigger rules for various diseases.
Whatever the precise merits, features, and advantages of the above cited references, none of them achieves or fulfills the purposes of the present invention.